# Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion

arXiv: https://arxiv.org/abs/2012.07011 · HTML: https://mlsanity.com/paper/2012.07011/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2304.12604/) (2023)
- [Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2309.03251/) (2023)
- [Rethinking Graph Contrastive Learning through Relative Similarity Preservation](https://mlsanity.com/paper/2505.05533/) (2025)
- [Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking](https://mlsanity.com/paper/2505.19722/) (2025)
- [Incomplete Knowledge Graph Alignment](https://mlsanity.com/paper/2112.09266/) (2021)
- [Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching](https://mlsanity.com/paper/2604.05866/) (2026)

## Abstract (arXiv)

Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the contextual information of entity and relation. Here, we extract contexts of entities and relations from the triplets which they compose. We propose a model named AggrE, which conducts efficient aggregations respectively on entity context and relation context in multi-hops, and learns context-enhanced entity and relation embeddings for knowledge graph completion. The experiment results show that AggrE is competitive to existing models.